A biological marker model for predicting disease transitions

For patients with chronic myelogenous leukemia (CML), the effect of elevated blood levels of adenosine deaminase (ADA) is studied as a marker for transitions from stable disease to blast crisis and then to death. Data in the form of snapshots over time, with day, state of disease, and ADA level, are analyzed for 55 patients. A simple three-state Markov model with one-way transition probabilities dependent on ADA is used to determine if the marker has a significant effect on the prediction of changes from stable disease to blast crisis.     

A metabolomics-based approach for predicting stages of chronic kidney disease

Chronic kidney disease (CKD) is a major epidemiologic problem and a risk factor for cardiovascular events and cerebrovascular accidents. Because CKD shows irreversible progression, early diagnosis is desirable. Renal function can be evaluated by measuring creatinine-based estimated glomerular filtration rate (eGFR). This method, however, has low sensitivity during early phases of CKD. Cystatin C (CysC) may be a more sensitive predictor. Using a metabolomic method, we previously identified metabolites in CKD and hemodialysis patients. To develop a new index of renal hypofunction, plasma samples were collected from volunteers with and without CKD and metabolite concentrations were assayed by quantitative liquid chromatography/mass spectrometry. These results were used to construct a multivariate regression equation for an inverse of CysC-based eGFR, with eGFR and CKD stage calculated from concentrations of blood metabolites. This equation was able to predict CKD stages with 81.3% accuracy (range, 73.9–87.0% during 20 repeats). This procedure may become a novel method of identifying patients with early-stage CKD.     

Accuracy of predicting the genetic risk of disease using a genome-wide approach

Background

The prediction of the genetic disease risk of an individual is a powerful public health tool. While predicting risk has been successful in diseases which follow simple Mendelian inheritance, it has proven challenging in complex diseases for which a large number of loci contribute to the genetic variance. The large numbers of single nucleotide polymorphisms now available provide new opportunities for predicting genetic risk of complex diseases with high accuracy.

Methodology/Principal Findings

We have derived simple deterministic formulae to predict the accuracy of predicted genetic risk from population or case control studies using a genome-wide approach and assuming a dichotomous disease phenotype with an underlying continuous liability. We show that the prediction equations are special cases of the more general problem of predicting the accuracy of estimates of genetic values of a continuous phenotype. Our predictive equations are responsive to all parameters that affect accuracy and they are independent of allele frequency and effect distributions. Deterministic prediction errors when tested by simulation were generally small. The common link among the expressions for accuracy is that they are best summarized as the product of the ratio of number of phenotypic records per number of risk loci and the observed heritability.

Conclusions/Significance

This study advances the understanding of the relative power of case control and population studies of disease. The predictions represent an upper bound of accuracy which may be achievable with improved effect estimation methods. The formulae derived will help researchers determine an appropriate sample size to attain a certain accuracy when predicting genetic risk.     

A training primer for Institutional Officials

The laws and policies governing the care and use of animals in research in the US require institutions to establish training programs to assure that personnel are qualified for their roles in animal care and use programs. Few programs define specific training requirements for the Institutional Official (IO), one of the most important roles in an animal care program. In some cases, IOs may have little or no experience in biomedical science. In this article, the author provides an overview of the IO's role in an animal care and use program as defined by US government laws and policies for use in training IOs and chief executive officers. The author outlines the key responsibilities of the IO in an animal care program, the implications of noncompliance with federal requirements and some of the pitfalls in program design.     

A risk signature with four autophagy-related genes for predicting survival of glioblastoma multiforme

Glioblastoma multiforme (GBM) is a devastating brain tumour without effective treatment. Recent studies have shown that autophagy is a promising therapeutic strategy for GBM. Therefore, it is necessary to identify novel biomarkers associated with autophagy in GBM. In this study, we downloaded autophagy-related genes from Human Autophagy Database (HADb) and Gene Set Enrichment Analysis (GSEA) website. Least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis were performed to identify genes for constructing a risk signature. A nomogram was developed by integrating the risk signature with clinicopathological factors. Time-dependent receiver operating characteristic (ROC) curve and calibration plot were used to evaluate the efficiency of the prognostic model. Finally, four autophagy-related genes (DIRAS3, LGALS8, MAPK8 and STAM) were identified and were used for constructing a risk signature, which proved to be an independent risk factor for GBM patients. Furthermore, a nomogram was developed based on the risk signature and clinicopathological factors (IDH1 status, age and history of radiotherapy or chemotherapy). ROC curve and calibration plot suggested the nomogram could accurately predict 1-, 3- and 5-year survival rate of GBM patients. For function analysis, the risk signature was associated with apoptosis, necrosis, immunity, inflammation response and MAPK signalling pathway. In conclusion, the risk signature with 4 autophagy-related genes could serve as an independent prognostic factor for GBM patients. Moreover, we developed a nomogram based on the risk signature and clinical traits which was validated to perform better for predicting 1-, 3- and 5-year survival rate of GBM.     

A primer in epidemiologic methodology

Epidemiology is defined as the study of the distribution and determinants of disease within populations. In addition to the requirements for disease surveillance, epidemiologic methods have numerous applications in laboratory animal science and can reveal important insights into the multifactoral mechanisms of disease, thereby aiding in the design of optimized intervention strategies. Observational approaches to data collection can be used to quantify the role of causal factors under natural circumstances, complementing the value of experimental studies in this field. The meaning and appropriate use of standard measures of disease frequency and exposure-disease relationships are reviewed, along with explanations of bias and confounding. Recommendations for reporting the methods and findings from this type of work in comparative medicine literature are presented. Aspects of model-based approaches to data analysis are introduced, offering further opportunities for gaining needed information from epidemiologic study of problems in laboratory animal medicine and management.     

Serum TNFRII: A promising biomarker for predicting the risk of subcentimetre lung adenocarcinoma

Early diagnosis of lung adenocarcinoma requires effective risk predictors. TNFRII was reported to be related to tumorigenesis, but remained unclear in lung cancer. This research set out to investigate the relationship between the sTNFRII (serum TNFRII) level and the risk of lung adenocarcinoma less than 1 cm in diameter. Seventy-one pairs of subcentimetre lung adenocarcinoma patients and healthy controls were analysed through multiplex bead-based Luminex assay and found a significantly lower expression of sTNFRII in patients with subcentimetre lung adenocarcinoma than that in the healthy controls (P < .001), which was further verified through ONCOMINE database analysis. Increased levels of sTNFRII reduced the risk of subcentimetre lung adenocarcinoma by 89% (P < .001). Patients with a higher level of BLC had a 2.70-fold (P < .01) higher risk of subcentimetre adenocarcinoma. Furthermore, a higher BLC/TNFRII ratio was related to a 35-fold higher risk of subcentimetre adenocarcinoma. TNFRII showed good specificity, sensitivity and accuracy (0.72, 0.75 and 0.73, respectively), with an AUC of 0.73 (P < .001). In conclusion, the present study assessed the value of sTNFRII as a potential biomarker to predict the risk of subcentimetre lung adenocarcinoma and provided evidence for the further use of TNFRII as an auxiliary marker in the diagnosis of subcentimetre lung adenocarcinoma.     

A bioinformatics method for predicting long noncoding RNAs associated with vascular disease

Long noncoding RNAs (lncRNAs) play important roles in human diseases including vascular disease. Given the large number of lncRNAs, however, whether the majority of them are associated with vascular disease remains unknown. For this purpose, here we present a genomic location based bioinformatics method to predict the lncRNAs associated with vascular disease. We applied the presented method to globally screen the human lncRNAs potentially involved in vascular disease. As a result, we predicted 3043 putative vascular disease associated lncRNAs. To test the accuracy of the method, we selected 10 lncRNAs predicted to be implicated in proliferation and migration of vascular smooth muscle cells (VSMCs) for further experimental validation. The results confirmed that eight of the 10 lncRNAs (80%) are validated. This result suggests that the presented method has a reliable prediction performance. Finally, the presented bioinformatics method and the predicted vascular disease associated lncRNAs together may provide helps for not only better understanding of the roles of lncRNAs in vascular disease but also the identification of novel molecules for the diagnosis and therapy of vascular disease.     

A universal procedure for primer labelling of amplicons.

Detection and visualisation of nucleic acids is integral to genome analyses. Exponential amplification procedures have provided the means for the manipulation of nucleic acid sequences, which were otherwise inaccessible. We describe the development and application of a universal method for the labelling of any PCR product using a single end-labelled primer. Amplification was performed in a single reaction with the resulting amplicon labelled to a high specific activity. The method was adapted to a wide range of PCRs and significantly reduced the expense of such analyses.